Atomistic Molecular Dynamics (MD) simulations provide researchers the ability to model biomolecular structures such as proteins and their interactions with drug-like small molecules with greater spatiotemporal resolution than is otherwise possible using experimental methods. MD simulations are notoriously expensive computational endeavors that have traditionally required massive investment in specialized hardware to access biologically relevant spatiotemporal scales. Our goal is to summarize the fundamental algorithms that are employed in the literature to then highlight the challenges that have affected accelerator implementations in practice. We consider three broad categories of accelerators: Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application Specific Integrated Circuits (ASICs). These categories are comparatively studied to facilitate discussion of their relative trade-offs and to gain context for the current state of the art. We conclude by providing insights into the potential of emerging hardware platforms and algorithms for MD.

中文翻译：

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用于生物分子经典分子动力学模拟的加速器


原子分子动力学 (MD) 模拟使研究人员能够对蛋白质等生物分子结构及其与药物样小分子的相互作用进行建模，其时空分辨率比使用实验方法时更高。 MD 模拟是众所周知的昂贵的计算工作，传统上需要对专用硬件进行大量投资才能获得生物相关的时空尺度。我们的目标是总结文献中使用的基本算法，然后强调在实践中影响加速器实现的挑战。我们考虑三大类加速器：图形处理单元 (GPU)、现场可编程门阵列 (FPGA) 和专用集成电路 (ASIC)。对这些类别进行比较研究，以促进对其相对权衡的讨论，并了解当前技术水平的背景。最后，我们深入探讨了 MD 的新兴硬件平台和算法的潜力。